Hassan Bagher-Ebadian1,
2, Rajan Jain1, Jayant Narang1, Hamid
Soltanian-Zadeh1, 3
1Radiology,
Henry Ford Hospital, Detroit, MI, United States; 2Physics, Oakland
University, Rochester, MI, United States; 3CIPCE-Department of
ECE, University of Tehran, Tehran, Iran
This study investigates feasibility of using a model-trained Adaptive Model to estimate Mean-Transit-Time (MTT) and relative-Cerebral-Blood-Flow (rCBF) in Dynamic-Susceptibility (DSC)-MR perfusion studies. A residue function with an exponential kernel was used to model the T2*-weighted-images with bolus passage. The ANN was trained and validated using a set of central moments and K-Folding-Cross-Validation (KFCV) technique. DSC-MR perfusion and CT-perfusion studies of four patents were analyzed using the ANN and Singular-Value-Decomposition technique. Results imply that the ANN produces accurate and stable hemodynamic maps compared to the SVD technique which can be used as a fast and accurate estimator in DSC perfusion studies.